Use of taxonomized analytics reference model

ABSTRACT

The composition of a data-driven analytics model that includes at least an analytical modeling component that defines analytical relationships between the model parameters using multiple analytical relations. The analytical modeling component uses the analytical relations to identify which of the model parameters are known and which are unknown, and solves for the identified unknown model parameter(s). The analytics modeling component also includes an analytics taxonomy in which the analytical relations are categorized into related analytics categories. Navigation through the analytics taxonomy assists in the composition of an analytics model. The analytics taxonomy may, but need not, be domain specific.

BACKGROUND

Often, the most effective way to convey information to a human being is visually. Accordingly, millions of people work with a wide range of visual items in order to convey or receive information, and in order to collaborate. Such visual items might include, for example, concept sketches, engineering drawings, explosions of bills of materials, three-dimensional models depicting various structures such as buildings or molecular structures, training materials, illustrated installation instructions, planning diagrams, and so on.

More recently, these visual items are constructed electronically using, for example, Computer Aided Design (CAD) and solid modeling applications. Often these applications allow authors to attach data and constraints to the geometry. For instance, the application for constructing a bill of materials might allow for attributes such as part number and supplier to be associated with each part, the maximum angle between two components, or the like. An application that constructs an electronic version of an arena might have a tool for specifying a minimum clearance between seats, and so on.

Such applications have contributed enormously to the advancement of design and technology. However, any given application does have limits on the type of information that can be visually conveyed, how that information is visually conveyed, or the scope of data and behavior that can be attributed to the various visual representations. If the application is to be modified to go beyond these limits, a new application would typically be authored by a computer programmer which expands the capabilities of the application, or provides an entirely new application. Also, there are limits to how much a user (other than the actual author of the model) can manipulate the model to test various scenarios.

BRIEF SUMMARY

Embodiments described herein relate to the composing of a data-driven analytics model that includes at least an analytical modeling component that defines analytical relationships between the model parameters using multiple analytical relations. The analytical modeling component uses the analytical relations to identify which of the model parameters are known and which are unknown, and solves for the identified unknown model parameter(s). The analytics modeling component also includes an analytics taxonomy in which the analytical relations categorized into related analytics categories. Navigation through the analytics taxonomy assists in the composition of a domain specific analytics model. In one embodiment, the analytics taxonomy is domain specific.

This Summary is not intended to identify key features or essential features of the claimed subject matter, nor is it intended to be used as an aid in determining the scope of the claimed subject matter.

BRIEF DESCRIPTION OF THE DRAWINGS

In order to describe the manner in which the above-recited and other advantages and features can be obtained, a more particular description of various embodiments will be rendered by reference to the appended drawings. Understanding that these drawings depict only sample embodiments and are not therefore to be considered to be limiting of the scope of the invention, the embodiments will be described and explained with additional specificity and detail through the use of the accompanying drawings in which:

FIG. 1 illustrates an environment in which the principles of the present invention may be employed including a data-driven composition framework that constructs a view composition that depends on input data;

FIG. 2 illustrates a pipeline environment that represents one example of the environment of FIG. 1;

FIG. 3 schematically illustrates an embodiment of the data portion of the pipeline of FIG. 2;

FIG. 4 schematically illustrates an embodiment of the analytics portion of the pipeline of FIG. 2;

FIG. 5 schematically illustrates an embodiment of the view portion of the pipeline of FIG. 2;

FIG. 6 illustrates a rendering of a view composition that may be constructed by the pipeline of FIG. 2;

FIG. 7 illustrates a flowchart of a method for generating a view composition using the pipeline environment of FIG. 2;

FIG. 8 illustrates a flowchart of a method for regenerating a view composition in response to user interaction with the view composition using the pipeline environment of FIG. 2;

FIG. 9 schematically illustrates the solver of the analytics portion of FIG. 4 in further detail including a collection of specialized solvers;

FIG. 10 illustrates a flowchart of the solver of FIG. 9 solving for unknown model parameters by coordinating the actions of a collection of specialized solvers;

FIG. 11 illustrates a rendering of an integrated view composition that extends the example of FIG. 6;

FIG. 12 illustrates a visualization of a shelf layout and represents just one of countless applications that the principles described herein may apply to;

FIG. 13 illustrates a visualization of an urban plan that the principles described herein may also apply to;

FIG. 14 illustrates a conventional visualization comparing children's education, that the principles of the present invention may apply to thereby creating a more dynamic learning environment;

FIG. 15 illustrates a conventional visualization comparing population density, that the principles of the present invention may apply to thereby creating a more dynamic learning environment;

FIG. 16 illustrates a taxonomy environment in which the taxonomy component of FIG. 2 may operate;

FIG. 17 illustrates an example of a taxonomy of the member items of FIG. 16;

FIGS. 18A through 18C show three examples of taxonomies of related categories;

FIG. 19 illustrates a member item that includes multiple properties;

FIG. 20 illustrates a domain-specific taxonomy and represents one example of the domain-specified taxonomies of FIG. 16;

FIG. 21 illustrates a flowchart of a method for navigating and using analytics; and

FIG. 22 illustrates a computing system that represents an environment in which the composition framework of FIG. 1 (or portions thereof) may be implemented.

DETAILED DESCRIPTION

FIG. 1 illustrates a visual composition environment 100 that uses data-driven analytics and visualization of the analytical results. The environment 100 (also called hereinafter a “pipeline”) includes a composition framework 110 that performs logic that is performed independent of the problem-domain of the view construction 130. For instance, the same composition framework 110 may be used to compose interactive view compositions for city plans, molecular models, grocery shelf layouts, machine performance or assembly analysis, or other domain-specific renderings. In order to efficiently organize data, analytic and visual components in such an environment, domain-specific taxonomical categorizations of the data, analytics and visual components may also be employed as described in detail herein. First, however, the basic composition framework 110 will be described in detail.

The composition framework 110 uses domain-specific data 120 that is taxonomically organized in a domain-specific way to construct the actual visual construction 130 that is specific to the domain. Accordingly, the same composition framework 110 may be used to construct view compositions for any number of different domains by changing the domain-specific data 120, rather than having to recode the composition framework 110 itself. Thus, the composition framework 110 of the pipeline 100 may apply to a potentially unlimited number of problem domains, or at least to a wide variety of problem domains, by altering data, rather than recoding and recompiling. The view construction 130 may then be supplied as instructions to an appropriate 2-D or 3-D rendering module. The architecture described herein also allows for convenient incorporation of pre-existing view compositions as building blocks to new view compositions. In one embodiment, multiple view compositions may be included in an integrated view composition to allow for easy comparison between two possible solutions to a model.

FIG. 2 illustrates an example architecture of the composition framework 110 in the form of a pipeline environment 200. The pipeline environment 200 includes, amongst other things, the pipeline 201 itself. The pipeline 201 includes a data portion 210, an analytics portion 220, and a view portion 230, which will each be described in detail with respect to subsequent FIGS. 3 through 5, respectively, and the accompanying description. For now, at a general level, the data portion 210 of the pipeline 201 may accept a variety of different types of data and presents that data in a canonical form to the analytics portion 220 of the pipeline 201. The analytics portion 220 binds the data to various model parameters, and solves for the unknowns in the model parameters using model analytics. The various parameter values are then provided to the view portion 230, which constructs the composite view using those values of the model parameters.

The pipeline environment 200 also includes an authoring component 240 that allows an author or other user of the pipeline 201 to formulate and/or select data to provide to the pipeline 201. For instance, the authoring component 240 may be used to supply data to each of data portion 210 (represented by input data 211), analytics portion 220 (represented by analytics data 221), and view portion 230 (represented by view data 231). The various data 211, 221 and 231 represent an example of the domain-specific data 120 of FIG. 1, and will be described in much further detail hereinafter. The authoring component 240 supports the providing of a wide variety of data including for example, data schemas, actual data to be used by the model, the location or range of possible locations of data that is to be brought in from external sources, visual (graphical or animation) objects, user interface interactions that can be performed on a visual, modeling statements (e.g., views, equations, constraints), bindings, and so forth.

In one embodiment, the authoring component is but one portion of the functionality provided by an overall manager component (not shown in FIG. 2, but represented by the composition framework 110 of FIG. 1). The manager is an overall director that controls and sequences the operation of all the other components (such as data connectors, solvers, viewers, and so forth) in response to events (such as user interaction events, external data events, and events from any of the other components such as the solvers, the operating system, and so forth).

Traditionally, the lifecycle of an interactive view composition application involves two key times: authoring time, and use time. At authoring time, the functionality of the interactive view composition application is coded by a programmer to provide an interactive view composition that is specific to the desired domain. For instance, the author of an interior design application (e.g., typically, a computer programmer) might code an application that permits a user to perform a finite set of actions specific to interior designing.

At use time, a user (e.g., perhaps a home owner or a professional interior designer) might then use the application to perform any one or more of the set of finite actions that are hard coded into the application. In the interior design application example, the user might specify the dimensions of a virtual room being displayed, add furniture and other interior design components to the room, perhaps rotate the view to get various angles on the room, set the color of each item, and so forth. However, unless the user is a programmer that does not mind reverse-engineering and modifying the interior design application, the user is limited to the finite set of actions that were enabled by the application author. For example, unless offered by the application, the user would not be able to use the application to automatically figure out which window placement would minimize ambient noise, how the room layout performs according to Feng Shui rules, or minimize solar heat contribution.

However, in the pipeline environment 200 of FIG. 2, the authoring component 240 is used to provide data to an existing pipeline 201, where it is the data that drives the entire process from defining the input data, to defining the analytical model, to defining how the results of the analytics are visualized in the view composition. Accordingly, one need not perform any coding in order to adapt the pipeline 201 to any one of a wide variety of domains and problems. Only the data provided to the pipeline 201 is what is to change in order to apply the pipeline 201 to visualize a different view composition either from a different problem domain altogether, or to perhaps adjust the problem solving for an existing domain. The pipeline environment 200 may also include a taxonomy component 260 that organizes, categorizes, and relates data that is provided to the pipeline 201. The taxonomy component 260 may be sensitive to the domain. Thus, an interior design domain, a road design domain, an architecture domain, a Feng Shui domain may each have a different taxonomy that may be used to navigate through the data. This can be helpful since, as will be described below, there may be considerable data available to sift through due to composition activity in which the data set that is made available to the pipeline environment 200 may be ever increasing.

Further, since the data can be changed at use time (i.e., run time), as well as at author time, the model can be modified and/or extended at runtime. Thus, there is less, if any, distinction between authoring a model and running the model. Because all authoring involves editing data items and because the software runs all of its behavior from data, every change to data immediately affects behavior without the need for recoding and recompilation.

The pipeline environment 200 also includes a user interaction response module 250 that detects when a user has interacted with the displayed view composition, and then determines what to do in response. For example, some types of interactions might require no change in the data provided to the pipeline 201 and thus require no change to the view composition. Other types of interactions may change one or more of the data 211, 221, or 231. In that case, this new or modified data may cause new input data to be provided to the data portion 210, might require a reanalysis of the input data by the analytics portion 220, and/or might require a re-visualization of the view composition by the view portion 230.

Accordingly, the pipeline 201 may be used to extend data-driven analytical visualizations to perhaps an unlimited number of problem domains, or at least to a wide variety of problem domains. Furthermore, one need not be a programmer to alter the view composition to address a wide variety of problems. Each of the data portion 210, the analytics portion 220 and the view portion 230 of the pipeline 201 will now be described with respect to the data portion 300 of FIG. 3, the analytics portion 400 of FIG. 4, and the view portion 500 of FIG. 5, in that order. In addition, the taxonomy of the data is specific to the domain, thus allowing the organization of the data to be more intuitive to those operating in the domain. As will be apparent from FIGS. 3 through 5, the pipeline 201 may be constructed as a series of transformation components where they each 1) receive some appropriate input data, 2) perform some action in response to that input data (such as performing a transformation on the input data), and 3) output data which then serves as input data to the next transformation component.

The pipeline 201 may be implemented on the client, on the server, or may even be distributed amongst the client and the server without restriction. For instance, the pipeline 201 might be implemented on the server and provide rendering instructions as output. A browser at the client-side may then just render according to the rendering instructions received from the server. At the other end of the spectrum, the pipeline 201 may be contained on the client with authoring and/or use performed at the client. Even if the pipeline 201 was entirely at the client, the pipeline 201 might still search data sources external to the client for appropriate information (e.g., models, connectors, canonicalizers, schemas, and others). There are also embodiments that provide a hybrid of these two approaches. For example, in one such hybrid approach, the model is hosted on a server but web browser modules are dynamically loaded on the client so that some of the model's interaction and viewing logic is made to run on the client (thus allowing richer and faster interactions and views).

FIG. 3 illustrates just one of many possible embodiments of a data portion 300 of the pipeline 201 of FIG. 2. One of the functions of the data portion 300 is to provide data in a canonical format that is consistent with schemas understood by the analytics portion 400 of the pipeline discussed with respect to FIG. 4. The data portion includes a data access component 310 that accesses the heterogenic data 301. The input data 301 may be “heterogenic” in the sense that the data may (but need not) be presented to the data access component 310 in a canonical form. In fact, the data portion 300 is structured such that the heterogenic data could be of a wide variety of formats. Examples of different kinds of domain data that can be accessed and operated on by models include text and XML documents, tables, lists, hierarchies (trees), SQL database query results, BI (business intelligence) cube query results, graphical information such as 2D drawings and 3D visual models in various formats, and combinations thereof (i.e., a composite). Further, the kind of data that can be accessed can be extended declaratively, by providing a definition (e.g., a schema) for the data to be accessed. Accordingly, the data portion 300 permits a wide variety of heterogenic input into the model, and also supports runtime, declarative extension of accessible data types.

In one embodiment, the data access portion 300 includes a number of connectors for obtaining data from a number of different data sources. Since one of the primary functions of the connector is to place corresponding data into canonical form, such connectors will often be referred to hereinafter and in the drawings as “canonicalizers”. Each canonicalizer might have an understanding of the specific Application Program Interfaces (API's) of its corresponding data source. The canonicalizer might also include the corresponding logic for interfacing with that corresponding API to read and/or write data from and to the data source. Thus, canonicalizers bridge between external data sources and the memory image of the data.

The data access component 310 evaluates the input data 301. If the input data is already canonical and thus processable by the analytics portion 400, then the input data may be directly provided as canonical data 340 to be input to the analytics portion 400.

However, if the input data 301 is not canonical, then the appropriate data canonicalization component 330 is able to convert the input data 301 into the canonical format. The data canonicalization components 330 are actually a collection of data canonicalization components 330, each capable of converting input data having particular characteristics into canonical form. The collection of canonicalization components 330 is illustrated as including four canonicalization components 331, 332, 333 and 334. However, the ellipsis 335 represents that there may be other numbers of canonicalization components as well, perhaps even fewer that the four illustrated.

The input data 301 may even include a canonicalizer itself as well as an identification of correlated data characteristic(s). The data portion 300 may then register the correlated data characteristics, and provide the canonicalization component to the data canonicalization component collection 330, where it may be added to the available canonicalization components. If input data is later received that has those correlated characteristics, the data portion 310 may then assign the input data to the correlated canonicalization component. Canonicalization components can also be found dynamically from external sources, such as from defined component libraries on the web. For example, if the schema for a given data source is known but the needed canonicalizer is not present, the canonicalizer can be located from an external component library, provided such a library can be found and contains the needed components. The pipeline might also parse data for which no schema is yet known and compare parse results versus schema information in known component libraries to attempt a dynamic determination of the type of the data, and thus to locate the needed canonicalizer components.

Alternatively, instead of the input data including all of the canonicalization components, the input data may instead provide a transformation definition defining canonicalization transformations. The collection 330 may then be configured to convert that transformations definition into a corresponding canonicalization component that enforces the transformations along with zero or more standard default canonicalization transformation. This represents an example of a case in which the data portion 300 consumes the input data and does not provide corresponding canonicalized data further down the pipeline. In perhaps most cases, however, the input data 301 results in corresponding canonicalized data 340 being generated.

In one embodiment, the data portion 310 may be configured to assign input data to the data canonicalization component on the basis of a file type and/or format type of the input data. Other characteristics might include, for example, the source of the input data. A default canonicalization component may be assigned to input data that does not have a designated corresponding canonicalization component. The default canonicalization component may apply a set of rules to attempt to canonicalize the input data. If the default canonicalization component is not able to canonicalize the data, the default canonicalization component might trigger the authoring component 240 of FIG. 2 to prompt the user to provide a schema definition for the input data. If a schema definition does not already exist, the authoring component 240 might present a schema definition assistant to help the author generate a corresponding schema definition that may be used to transform the input data into canonical form. Once the data is in canonical form, the schema that accompanies the data provides sufficient description of the data that the rest of the pipeline 201 does not need new code to interpret the data. Instead, the pipeline 201 includes code that is able to interpret data in light of any schema that is expressible an accessible schema declaration language.

Regardless, canonical data 340 is provided as output data from the data portion 300 and as input data to the analytics portion 400. The canonical data might include fields that include a variety of data types. For instance, the fields might include data types such as integers, floating point numbers, strings, vectors, arrays, collections, hierarchical structures, text, XML documents, tables, lists, SQL database query results, BI (business intelligence) cube query results, graphical information such as 2D drawings and 3D visual models in various formats, or even complex combinations of these various data types. As another advantage, the canonicalization process is able to canonicalize a wide variety of input data. Furthermore, the variety of input data that the data portion 300 is able to accept is expandable. This is helpful in the case where multiple models are combined as will be discussed later in this description.

FIG. 4 illustrates analytics portion 400 which represents an example of the analytics portion 220 of the pipeline 201 of FIG. 2. The data portion 300 provided the canonicalized data 401 to the data-model binding component 410. The canonicalized data 401 might have any canonicalized form, and any number of parameters, where the form and number of parameters might even differ from one piece of input data to another. For purposes of discussion, however, the canonical data 401 has fields 402A through 402H, which may collectively be referred to herein as “fields 402”.

On the other hand, the analytics portion 400 includes a number of model parameters 411. The type and number of model parameters may differ according to the model. However, for purposes of discussion of a particular example, the model parameters 411 will be discussed as including model parameters 411A, 411B, 411C and 411D. In one embodiment, the identity of the model parameters, and the analytical relationships between the model parameters may be declaratively defined without using imperative coding.

A data-model binding component 410 intercedes between the canonicalized data fields 402 and the model parameters 411 to thereby provide bindings between the fields. In this case, the data field 402B is bound to model parameter 411A as represented by arrow 403A. In other words, the value from data field 402B is used to populate the model parameter 411A. Also, in this example, the data field 402E is bound to model parameter 411B (as represented by arrow 403B), and data field 402H is bound to model parameter 411C (as represented by arrow 403C).

The data fields 402A, 402C, 402D, 402F and 402G are not shown bound to any of the model parameters. This is to emphasize that not all of the data fields from input data are always required to be used as model parameters. In one embodiment, one or more of these data fields may be used to provide instructions to the data-model binding component 410 on which fields from the canonicalized data (for this canonicalized data or perhaps any future similar canonicalized data) are to be bound to which model parameter. This represents an example of the kind of analytics data 221 that may be provided to the analytics portion 220 of FIG. 2. The definition of which data fields from the canonicalized data are bound to which model parameters may be formulated in a number of ways. For instance, the bindings may be 1) explicitly set by the author at authoring time, 2) explicitly set by the user at use time (subject to any restrictions imposed by the author), 3) automatic binding by the authoring component 240 based on algorithmic heuristics, and/or 4) prompting by the authoring component of the author and/or user to specify a binding when it is determined that a binding cannot be made algorithmically. Thus bindings may also be resolved as part of the model logic itself.

The ability of an author to define which data fields are mapped to which model parameters gives the author great flexibility in being able to use symbols that the author is comfortable with to define model parameters. For instance, if one of the model parameters represents pressure, the author can name that model parameter “Pressure” or “P” or any other symbol that makes sense to the author. The author can even rename the model parameter which, in one embodiment, might cause the data model binding component 410 to automatically update to allow bindings that were previously to the model parameter of the old name to instead be bound to the model parameter of the new name, thereby preserving the desired bindings. This mechanism for binding also allows binding to be changed declaratively at runtime.

The model parameter 411D is illustrated with an asterisk to emphasize that in this example, the model parameter 411D was not assigned a value by the data-model binding component 410. Accordingly, the model parameter 411D remains an unknown. In other words, the model parameter 411D is not assigned a value.

The modeling component 420 performs a number of functions. First, the modeling component 420 defines analytical relationships 421 between the model parameters 411. The analytical relationships 421 are categorized into three general categories including equations 431, rules 432 and constraints 433. However, the list of solvers is extensible. In one embodiment, for example, one or more simulations may be incorporated as part of the analytical relationships provided a corresponding simulation engine is provided and registered as a solver.

The term “equation” as used herein aligns with the term as it is used in the field of mathematics.

The term “rules” as used herein means a conditional statement where if one or more conditions are satisfied (the conditional or “if” portion of the conditional statement), then one or more actions are to be taken (the consequence or “then” portion of the conditional statement). A rule is applied to the model parameters if one or more model parameters are expressed in the conditional statement, or one or more model parameters are expressed in the consequence statement.

The term “constraint” as used herein means that a restriction is applied to one or more model parameters. For instance, in a city planning model, a particular house element may be restricted to placement on a map location that has a subset of the total possible zoning designations. A bridge element may be restricted to below a certain maximum length, or a certain number of lanes.

An author that is familiar with the model may provide expressions of these equations, rules and constraint that apply to that model. In the case of simulations, the author might provide an appropriate simulation engine that provides the appropriate simulation relationships between model parameters. The modeling component 420 may provide a mechanism for the author to provide a natural symbolic expression for equations, rules and constraints. For example, an author of a thermodynamics related model may simply copy and paste equations from a thermodynamics textbook. The ability to bind model parameters to data fields allows the author to use whatever symbols the author is familiar with (such as the exact symbols used in the author's relied-upon textbooks) or the exact symbols that the author would like to use.

Prior to solving, the modeling component 420 also identifies which of the model parameters are to be solved for (i.e., hereinafter, the “output model variable” if singular, or “output model variables” if plural, or “output model variable(s)” if there could be a single or plural output model variables). The output model variables may be unknown parameters, or they might be known model parameters, where the value of the known model parameter is subject to change in the solve operation. In the example of FIG. 4, after the data-model binding operation, model parameters 411A, 411B and 411C are known, and model parameter 411D is unknown. Accordingly, unknown model parameter 411D might be one of the output model variables. Alternatively or in addition, one or more of the known model parameters 411A, 411B and 411C might also be output model variables. The solver 440 then solves for the output model variable(s), if possible. In one embodiment described hereinafter, the solver 440 is able to solve for a variety of output model variables, even within a single model so long as sufficient input model variables are provided to allow the solve operation to be performed. Input model variables might be, for example, known model parameters whose values are not subject to change during the solve operation. For instance, in FIG. 4, if the model parameters 411A and 411D were input model variables, the solver might instead solve for output model variables 411B and 411C instead. In one embodiment, the solver might output any one of a number of different data types for a single model parameter. For instance, some equation operations (such as addition, subtraction, and the like) apply regardless of the whether the operands are integers, floating point, vectors of the same, or matrices of the same.

In one embodiment, even when the solver 440 cannot solve for a particular output model variable, the solver 400 might still present a partial solution for that output model variable, even if a full solve to the actual numerical result (or whatever the solved-for data type) is not possible. This allows the pipeline to facilitate incremental development by prompting the author as to what information is needed to arrive at a full solve. This also helps to eliminate the distinction between author time and use time, since at least a partial solve is available throughout the various authoring stages. For an abstract example, suppose that the analytics model includes an equation a=b+c+d. Now suppose that a, c and d are output model variables, and b is an input model variable having a known value of 5 (an integer in this case). In the solving process, the solver 440 is only able to solve for one of the output model variables “d”, and assign a value of 6 (an integer) to the model parameter called “d”, but the solver 440 is not able to solve for “c”. Since “a” depends from “c”, the model parameter called “a” also remains an unknown and unsolved for. In this case, instead of assigning an integer value to “a”, the solver might do a partial solve and output the string value of “c+11” to the model parameter “a”. As previously mentioned, this might be especially helpful when a domain expert is authoring an analytics model, and will essentially serve to provide partial information regarding the content of model parameter “a” and will also serve to cue the author that some further model analytics needs to be provided that allow for the “c” model parameter to be solved for. This partial solve result may be perhaps output in some fashion in the view composition to allow the domain expert to see the partial result.

The solver 440 is shown in simplified form in FIG. 4. However, the solver 440 may direct the operation of multiple constituent solvers as will be described with respect to FIG. 9. In FIG. 4, the modeling component 420 then makes the model parameters 411 (including the now known and solved-for output model variables) available as output to be provided to the view portion 500 of FIG. 5.

FIG. 5 illustrates a view portion 500 which represents an example of the view portion 230 of FIG. 2. The view portion 500 receives the model parameters 411 from the analytics portion 400 of FIG. 4. The view portion also includes a view components repository 520 that contains a collection of view components. For example, the view components repository 520 in this example is illustrated as including view components 521 through 524, although the view components repository 520 may contain any number of view components. The view components each may include zero or more input parameters. For example, view component 521 does not include any input parameters. However, view component 522 includes two input parameters 542A and 542B. View component 523 includes one input parameter 543, and view component 524 includes one input parameter 544. That said, this is just an example. The input parameters may, but need not necessarily, affect how the visual item is rendered. The fact that the view component 521 does not include any input parameters emphasizes that there can be views that are generated without reference to any model parameters. Consider a view that comprises just fixed (built-in) data that does not change. Such a view might for example constitute reference information for the user. Alternatively, consider a view that just provides a way to browse a catalog, so that items can be selected from it for import into a model.

Each view component 521 through 524 includes or is associated with corresponding logic that, when executed by the view composition component 540 using the corresponding view component input parameter(s), if any, causes a corresponding view item to be placed in virtual space 550. That virtual item may be a static image or object, or may be a dynamic animated virtual item or object For instance, each of view components 521 through 524 are associated with corresponding logic 531 through 534 that, when executed causes the corresponding virtual item 551 through 554, respectively, to be rendered in virtual space 550. The virtual items are illustrated as simple shapes. However, the virtual items may be quite complex in form perhaps even including animation. In this description, when a view item is rendered in virtual space, that means that the view composition component has authored sufficient instructions that, when provided to the rendering engine, the rendering engine is capable of displaying the view item on the display in the designated location and in the designated manner.

The view components 521 through 524 may be provided perhaps even as view data to the view portion 500 using, for example, the authoring component 240 of FIG. 2. For instance, the authoring component 240 might provide a selector that enables the author to select from several geometric forms, or perhaps to compose other geometric forms. The author might also specify the types of input parameters for each view component, whereas some of the input parameters may be default input parameters imposed by the view portion 500. The logic that is associated with each view component 521 through 524 may be provided with view data, and/or may also include some default functionality provided by the view portion 500 itself

The view portion 500 includes a model-view binding component 510 that is configured to bind at least some of the model parameters to corresponding input parameters of the view components 521 through 524. For instance, model parameter 411A is bound to the input parameter 542A of view component 522 as represented by arrow 511A. Model parameter 411B is bound to the input parameter 542B of view component 522 as represented by arrow 511B. Also, model parameter 411D is bound to the input parameters 543 and 544 of view components 523 and 524, respectively, as represented by arrow 511C. The model parameter 411C is not shown bound to any corresponding view component parameter, emphasizing that not all model parameters need be used by the view portion of the pipeline, even if those model parameters were essential in the analytics portion. Also, the model parameter 411D is shown bound to two different input parameters of view components representing that the model parameters may be bound to multiple view component parameters. In one embodiment, the definition of the bindings between the model parameters and the view component parameters may be formulated by 1) being explicitly set by the author at authoring time, 2) explicitly set by the user at use time (subject to any restrictions imposed by the author), 3) automatic binding by the authoring component 240 based on algorithmic heuristics, and/or 4) prompting by the authoring component of the author and/or user to specify a binding when it is determined that a binding cannot be made algorithmically.

As previously mentioned, the view item may include an animation. To take a simple example, consider for example a bar chart that plots a company's historical and projected revenues, advertising expenses, and profits by sales region at a given point in time (such as a given calendar quarter). A bar chart could be drawn for each calendar quarter in a desired time span. Now, imagine that you draw one of these charts, say the one for the earliest time in the time span, and then every half second replace it with the chart for the next time span (e.g., the next quarter). The result will be to see the bars representing profit, sales, and advertising expense for each region change in height as the animation proceeds. In this example, the chart for each time period is a “cell” in the animation, where the cell shows an instant between movements, where the collection of cells shown in sequence simulates movement. Conventional animation models allow for animation over time using built-in hard-coded chart types.

However, using the pipeline 201, by contrast, any kind of visual can be animated, and the animation can be driven by varying any one or any combination of the parameters of the visual component. To return to the bar chart example above, imagine that instead of animating by time, we animate by advertising expense. Each “cell” in this animation is a bar chart showing sales and profits over time for a given value of advertising expense. Thus, as the advertising expense is varied, the bars grow and shrink in response to the change in advertising expense.

The power of animated data displays is that they make very apparent to the eye what parameters are most sensitive to change in other parameters, because you immediately see how quickly and how far each parameter's values change in response to the varying of the animation parameter.

The pipeline 201 is also distinguished in its ability to animate due to the following characteristics:

First, the sequences of steps for the animation variable can be computed by the analytics of the model, versus being just a fixed sequence of steps over a predefined range. For example, in the example of varying the advertising expense as the animation variable, imagine that what is specified is to “animate by advertising expense where advertising expense is increased by 5% for each step” or “where advertising expense is 10% of total expenses for that step”. A much more sophisticated example is “animate by advertising expense where advertising expense is optimized to maximize the rate of change of sales over time”. In other words, the solver will determine a set of steps for advertising spend over time (i.e., for each successive time period such as quarter) such that the rate of growth of sales is maximized. Here the user presumably wants to see not only how fast sales can be made to grow by varying advertising expense, but also wants to learn the quarterly amounts for the advertising expenses that achieve this growth (the sequence of values could be plotted as part of the composite visual).

Second, any kind of visual can be animated, not just traditional data charts. For example, consider a Computer-Aided Design (CAD) model of a jet engine that is a) to be animated by the air speed parameter and 2) where the rotational speed of the turbine is a function of the air speed and 3) where the temperature of the turbine bearings is a function of the air speed. Jet engines have limits on how fast turbines can be rotated before either the turbine blades lose integrity or the bearing overheats. Thus, in this animation we desire that as air speed is varied the color of the turbine blades and bearing should be varied from blue (safe) to red (critical). The values for “safe” and “critical” turbine RPM and bearing temperature may well be calculated by the model based on physical characteristics of those parts. Now, as the animation varies the air speed over a defined range, we see the turbine blades and bearing each change color. What is now interesting is to notice which reaches critical first, and if either undergoes a sudden (runaway) run to critical. These kinds of effects are hard to discern by looking at a chart or at a sequence of drawings, but become immediately apparent in an animation. This is but one example of animating an arbitrary visual (CAD model) by an arbitrary parameter (air speed), with the animation affecting yet other arbitrary parameters (turbine RPM and bearing temp). Any parameter(s) of any visual(s) can be animated according to any desired parameter(s) that are to serve as the animation variables.

Third, the pipeline 201 can be stopped mid stream so that data and parameters may be modified by the user, and the animation then restarted or resumed. Thus, for example, in the jet engine example, if runaway heating is seen to start at a given air speed, the user may stop the animation at the point the runaway beings, modify some engine design criterion, such as the kind of bearing or bearing surface material, and then continue the animation to see the effect of the change.

As with other of the capabilities discussed herein, animations can be defined by the author, and/or left open for the user to manipulate to test various scenarios. For example, the model may be authored to permit some visuals to be animated by the user according to parameters the user himself selects, and/or over data ranges for the animation variable that the user selects (including the ability to specify computed ranges should that be desired). Such animations can also be displayed side by side as in the other what-if comparison displays. For example, a user could compare an animation of sales and profits over time, animated by time, in two scenarios with differing prevailing interest rates in the future, or different advertising expenses ramps. In the jet engine example, the user could compare the animations of the engine for both the before and after cases of changing the bearing design.

At this point, a specific example of how the composition framework may be used to actually construct a view composition will be described with respect to FIG. 6, which illustrated 3-D renderings 600 of a view composition that includes a room layout 601 with furniture laid out within the room, and also includes a Feng Shui meter 602. This example is provided merely to show how the principles described herein can apply to any arbitrary view composition, regardless of the domain. Accordingly, the example of FIG. 6, and any other example view composition described herein, should be viewed strictly as only an example that allows the abstract concept to be more fully understood by reference to non-limiting concrete examples, and not defining the broader scope of the invention. The principles described herein may apply to construct a countless variety of view compositions. Nevertheless, reference to a concrete example can clarify the broader abstract principles.

FIG. 7 illustrates a flowchart of a method 700 for generating a view construction. The method 700 may be performed by the pipeline environment 200 of FIG. 2, and thus will be described with frequent reference to the pipeline environment 200 of FIG. 2, as well as with reference to FIGS. 3 through 5, which each show specific portions of the pipeline of FIG. 2. While the method 700 may be performed to construct any view composition, the method 700 will be described with respect to the view composition 600 of FIG. 6. Some of the acts of the method 700 may be performed by the data portion 210 of FIG. 2 and are listed in the left column of FIG. 7 under the header “Data”. Other of the acts of the method 700 may be performed by the analytics portion 220 of FIG. 2, and are listed in the second from the left column of FIG. 7 under the header “Analytics”. Other of the acts of the method may be performed by the view portion 230 of FIG. 2, and are listed in the second from the right column under the header “View”. One of the acts may be performed by a rendering module and is listed in the right column under the header other. Any conventional or yet to be developed rendering module may be used to render a view composition constructed in accordance with the principles described herein.

Referring to FIG. 7, the data portion accesses input data that at least collectively affects what visual items are displayed or how a given one or more of the visual items are displayed (act 711). For instance, referring to FIG. 6, the input data might include view components for each of the items of furniture. For instance, each of the couch, the chair, the plants, the table, the flowers, and even the room itself may be represented by a corresponding view component. The view component might have input parameters that are suitable for the view component. If animation were employed, for example, some of the input parameters might affect the flow of the animation. Some of the parameters might affect the display of the visual item, and some parameters might not.

For instance, the room itself might be a view component. Some of the input parameters might include the dimensions of the room, the orientation of the room, the wall color, the wall texture, the floor color, the floor type, the floor texture, the position and power of the light sources in the room, and so forth. There might also be room parameters that do not necessarily get reflected in this view composition, but might get reflected in other views and uses of the room component. For instance, the room parameter might have a location of the room expressed in degrees, minutes, and seconds longitude and latitude. The room parameter might also include an identification of the author of the room component, and the average rental costs of the room.

The various components within the room may also be represented by a corresponding parameterized view component. For instance, each plant may be configured with an input parameter specifying a pot style, a pot color, pot dimensions, plant color, plant resiliency, plant dependencies on sunlight, plant daily water intake, plant daily oxygen production, plant position and the like. Once again, some of these parameters may affect how the display is rendered and others might not, depending on the nature of what is being displayed.

The Feng Shui meter 602 may also be a view component. The meter might include input parameters such as a diameter, a number of wedges to be contained in the diameter of the meter, a text color and the like. The various wedges of the Feng Shui meter may also be view components. In that case, the input parameters to the view components might be a title (e.g., water, mountain, thunder, wind, fire, earth, lake, heaven), perhaps a graphic to appear in the wedge, a color hue, or the like.

The analytics portion binds the input data to the model parameters (act 721), determines the output model variables (act 722), and uses the model-specific analytical relationships between the model parameters to solve for the output model variables (act 723). The binding operation of act 721 has been previously discussed, and essentially allows flexibility in allowing the author to define the model analytics equations, rules and constraints using symbols that the model author is comfortable with.

The identification of the output model variables may differ from one solving operation to the next. Even though the model parameters may stay the same, the identification of which model parameters are output model variables will depend on the availability of data to bind to particular model parameters. This has remarkable implications in terms of allowing a user to perform what-if scenarios in a given view composition.

For instance, in the Feng Shui room example of FIG. 6, suppose the user has bought a new chair to place in their living room. The user might provide the design of the room as data into the pipeline. This might be facilitated by the authoring component prompting the user to enter the room dimensions, and perhaps provide a selection tool that allows the user to select virtual furniture to drag and drop into the virtual room at appropriate locations that the actual furniture is placed in the actual room. The user might then select a piece of furniture that may be edited to have the characteristics of the new chair purchased by the user. The user might then drag and drop that chair into the room. The Feng Shui meter 602 would update automatically. In this case, the position and other attributes of the chair would be input model variables, and the Feng Shui scores would be output model variables. As the user drags the virtual chair to various positions, the Feng Shui scores of the Feng Shui meter would update, and the user could thus test the Feng Shui consequences of placing the virtual chair in various locations. To avoid the user from having to drag the chair to every possible location to see which gives the best Feng Shui, the user can get local visual clues (such as, for example, gradient lines or arrows) that tell the user whether moving the chair in a particular direction from its current location makes things better or worse, and how much better or worse.

However, the user could also do something else that is unheard of in conventional view composition. The user could actually change the output model variables. For instance, the user might indicate the desired Feng Shui score in the Feng Shui meter, and leave the position of the virtual chair as the output model variable. The solver would then solve for the output model variable and provide a suggested position or positions of the chair that would achieve at least the designated Feng Shui score. The user may choose to make multiple parameters output model variables, and the system may provide multiple solutions to the output model variables. This is facilitated by a complex solver that is described in further detail with respect to FIG. 9.

Returning to FIG. 7, once the output model variables are solved for, the model parameters are bound to the input parameters of the parameterized view components (act 731). For instance, in the Feng Shui example, after the unknown Feng Shui scores are solved for, the scores are bound as input parameters to Feng Shui meter view component, or perhaps to the appropriate wedge contained in the meter. Alternatively, if the Feng Shui scores were input model variables, the position of the virtual chair may be solved for and provided as an input parameter to the chair view component.

A simplified example will now be presented that illustrates the principles of how the solver can rearrange equations and change the designation of input and output model variables all driven off of one analytical model. The user herself does not have to rearrange the equations. The simplified example may not accurately represent Feng Shui rules, but illustrates the principle nevertheless. Suppose the total Feng Shui (FS) of the room (FSroom) equals the FS of a chair (FSchair) and the FS of a plant (FSplant). Suppose FSchair is equal to a constant A times the distance d of the chair from the wall. Suppose FSplant is a constant, B. The total FS of the room is then: FSroom=A*d+B. If d is an input model variable, then FSroom is an output model variable and its value, displayed on the meter, changes as the user repositions the chair. Now suppose the user clicks on the meter making it an input model variable and shifting d into unknown output model variable status. In this case, the solver effectively and internally rewrites the equation above as d=(FSroom−B)/A. In that case, the view component can move the chair around, changing d, its distance from the wall, as the user changes the desired value, FSroom, on the meter.

The view portion then constructs a view of the visual items (act 732) by executing the construction logic associated with the view component using the input parameter(s), if any, to perhaps drive the construction of the view item in the view composition. The view construction may then be provided to a rendering module, which then uses the view construction as rendering instructions (act 741).

In one embodiment, the process of constructing a view is treated as a data transformation that is performed by the solver. That is, for a given kind of view (e.g., consider a bar chart), there is a model consisting of rules, equations, and constraints that generates the view by transforming the input data into a displayable output data structure (called a scene graph) which encodes all the low level geometry and associated attributes needed by the rendering software to drive the graphics hardware. In the bar chart example, the input data would be for example the data series that is to be plotted, along with attributes for things like the chart title, axis labels, and so on. The model that generates the bar would have rules, equations, and constraints that would do things like 1) count how many entries the data series consists of in order to determine how many bars to draw, 2) calculate the range (min, max) that the data series spans in order to calculate things like the scale and starting/ending values for each axis, 3) calculate the height of the bar for each data point in the data series based on the previously calculated scale factor, 4) count how many characters are in the chart title in order to calculate a starting position and size for the title so that the title will be properly located and centered with respect to the chart, and so forth. In sum, the model is designed to calculate a set of geometric shapes based on the input data, with those geometric shapes arranged within a hierarchical data structure of type “scene graph”. In other words, the scene graph is an output variable that the model solves for based on the input data. Thus, an author can design entirely new kinds of views, customize existing views, and compose preexisting views into composites, using the same framework that the author uses to author, customize, and compose any kind of model. Thus, authors who are not programmers can create new views without drafting new code.

Returning to FIG. 2, recall that the user interaction response module 250 detects when the user interacts with the view composition, and causes the pipeline to respond appropriately. FIG. 8 illustrates a flowchart of a method 800 for responding to user interaction with the view composition. In particular, the user interaction response module determines which the components of the pipeline should perform further work in order to regenerate the view, and also provides data represented the user interaction, or that is at least dependent on the user interaction, to the pipeline components. In one embodiment, this is done via a transformation pipeline that runs in the reverse (upstream) view/analytics/data direction and is parallel to the (downstream) data/analytics/view pipeline.

Interactions are posted as events into the upstream pipeline. Each transformer in the data/analytics/view pipeline provides an upstream transformer that handles incoming interaction data. These transformers can either be null (passthroughs, which get optimized out of the path) or they can perform a transformation operation on the interaction data to be fed further upstream. This provides positive performance and responsiveness of the pipeline in that 1) interaction behaviors that would have no effect on upstream transformations, such as a view manipulation that has no effect on source data, can be handled at the most appropriate (least upstream) point in the pipeline and 2) intermediate transformers can optimize view update performance by sending heuristically-determined updates back downstream, ahead of the final updates that will eventually come from further upstream transformers. For example, upon receipt of a data edit interaction, a view-level transformer could make an immediate view update directly into the scene graph for the view (for edits it knows how to interpret), with the final complete update coming later from the upstream data transformer where the source data is actually edited.

When the semantics of a given view interaction have a nontrivial mapping to the needed underlying data edits, intermediate transformers can provide the needed upstream mapping. For example, dragging a point on a graph of a computed result could require a backwards solve that would calculate new values for multiple source data items that feed the computed value on the graph. The solver-level upstream transformer would be able to invoke the needed solve and to propagate upstream the needed data edits.

FIG. 8 illustrates a flowchart of a method 800 for responding to user interaction with the view construction. Upon detecting that the user has interacted with the rendering of a view composition on the display (act 801), it is first determined whether or not the user interaction requires regeneration of the view (decision block 802). This may be performed by the rendering engine raising an event that is interpreted by the user interaction response component 250 of FIG. 2. If the user interaction does not require regeneration of the view (No in decision block 802), then the pipeline does not perform any further action to reconstruct the view (act 803), although the rendering engine itself may perform some transformation on the view. An example of such a user interaction might be if the user were to increase the contrast of the rendering of the view construction, or rotate the view construction. Since those actions might be undertaken by the rendering engine itself, the pipeline need perform no work to reconstruct the view in response to the user interaction.

If, on the other hand, it is determined that the type of user interaction does require regeneration of the view construction (Yes in decision block 802), the view is reconstructed by the pipeline (act 804). This may involve some altering of the data provided to the pipeline. For instance, in the Feng Shui example, suppose the user were to move the position of the virtual chair within the virtual room, the position parameter of the virtual chair component would thus change. An event would be fired informing the analytics portion that the corresponding model parameter representing the position of the virtual chair should be altered as well. The analytics component would then resolve for the Feng Shui scores, repopulate the corresponding input parameters of the Feng Shui meter or wedges, causing the Feng Shui meter to update with current Feng Shui scores suitable for the new position of the chair.

The user interaction might require that model parameters that were previously known are now unknown, and that previously unknown parameters are now known. That is one of several possible examples that might require a change in designation of input and output model variables such that previously designated input model variables might become output model variables, and vice versa. In that case, the analytics portion would solve for the new output model variable(s) thereby driving the reconstruction of the view composition.

Solver Framework

FIG. 9 illustrates a solver environment 900 that may represent an example of the solver 440 of FIG. 4. The solver environment 900 may be implemented in software, hardware, or a combination. The solver environment 900 includes a solver framework 901 that manages and coordinates the operations of a collection 910 of specialized solvers. The collection 910 is illustrated as including three specialized solvers 911, 912 and 913, but the ellipsis 914 represents that there could be other numbers (i.e., more than three or less than three) of specialized solvers as well. Furthermore, the ellipsis 914 also represents that the collection 910 of specialized solves is extensible. As new specialized solvers are discovered and/or developed that can help with the model analytics, those new specialized solvers may be incorporated into the collection 910 to supplement the existing specialized solvers, or perhaps to replace one or more of the existing solvers. For example, FIG. 9 illustrates that a new solver 915 is being registered into the collection 910 using the solver registration module 921. As one example, a new solver might be perhaps a simulation solver which accepts one or more known values, and solves for one or more unknown values. Other examples include solvers for systems of linear equations, differential equations, polynomials, integrals, root-finders, factorizers, optimizers, and so forth. Every solver can work in numerical mode or in symbolic mode or in mixed numeric-symbolic mode. The numeric portions of solutions can drive the parameterized rendering downstream. The symbolic portions of the solution can drive partial solution rendering.

The collection of specialized solvers may include any solver that is suitable for solving for the output model variables. If, for example, the model is to determine drag of a bicycle, the solving of complex calculus equations might be warranted. In that case, a specialized complex calculus solver may be incorporated into the collection 910 to perhaps supplement or replace an existing equations solver. In one embodiment, each solver is designed to solve for one or more output model variables in a particular kind of analytics relationship. For example, there might be one or more equation solvers configured to solve for unknowns in an equation. There might be one or more rules solvers configured to apply rules to solve for unknowns. There might be one or more constraints solvers configured to apply constraints to thereby solve for unknowns. Other types of solves might be, for example, a simulation solver which performs simulations using input data to thereby construct corresponding output data.

The solver framework 901 is configured to coordinate processing of one or more or all of the specialized solvers in the collection 910 to thereby cause one or more output model variables to be solved for. The solver framework 901 is then configured to provide the solved for values to one or more other external components. For instance, referring to FIG. 2, the solver framework 901 may provide the model parameter values to the view portion 230 of the pipeline, so that the solving operation thereby affects how the view components execute to render a view item, or thereby affect other data that is associated with the view item. As another potential effect of solving, the model analytics themselves might be altered. For instance, as just one of many examples in which this might be implemented, the model might be authored with modifiable rules set so that, during a given solve, some rule(s) and/or constraint(s) that are initially inactive become activated, and some that are initially activated become inactivated. Equations can be modified this way as well.

FIG. 10 illustrates a flowchart of a method 1000 for the solver framework 901 to coordinate processing amongst the specialized solvers in the collection 910. The method 1000 of FIG. 10 will now be described with frequent reference to the solver environment 900 of FIG. 9.

The solver framework begins a solve operation by identifying which of the model parameters are input model variables (act 1001), and which of the model parameters are output model variables (act 1002), and by identifying the model analytics that define the relationship between the model parameters (act 1003). Given this information, the solver framework analyzes dependencies in the model parameters (act 1004). Even given a fixed set of model parameters, and given a fixed set of model analytics, the dependencies may change depending on which of the model parameters are input model variables and which are output model variables. Accordingly, the system can infer a dependency graph each time a solve operation is performed using the identity of which model parameters are input, and based on the model analytics. The user need not specify the dependency graph for each solve. By evaluating dependencies for every solve operation, the solver framework has the flexibility to solve for one set of one or more model variables during one solve operation, and solve for another set of one or more model variables for the next solve operation. In the context of FIGS. 2 through 5, that means greater flexibility for a user to specify what is input and what is output by interfacing with the view composition.

In some solve operations, the model may not have any output model variables at all. In that case, the solve will verify that all of the known model parameter values, taken together, satisfy all the relationships expressed by the analytics for that model. In other words, if you were to erase any one data value, turning it into an unknown, and then solve, the value that was erased would be recomputed by the model and would be the same as it was before. Thus, a model that is loaded can already exist in solved form, and of course a model that has unknowns and gets solves now also exists in solved form. What is significant is that a user interacting with a view of a solved model is nevertheless able to edit the view, which may have the effect of changing a data value or values, and thus cause a re-solve that will attempt to recompute data values for output model variables so that the new set of data values is consistent with the analytics. Which data values a user can edit (whether or not a model starts with output model variables) is controlled by the author; in fact, this is controlled by the author defining which variables represented permitted unknowns.

If there are expressions that have one or more unknowns that may be independently solved without first solving for other unknowns in other expressions (Yes in decision block 1005), then those expressions may be solved at any time (act 1006), even perhaps in parallel with other solving steps. On the other hand, if there are expressions that have unknowns that cannot be solved without first solving for an unknown in another expression, then a solve dependency has been found. In that case, the expression becomes part of a relational structure (such as a dependency tree) that defines a specific order of operation with respect to another expression.

In the case of expressions that have interconnected solve dependencies from other expressions, an order of execution of the specialized solvers is determined based on the analyzed dependencies (act 1007). The solvers are then executed in the determined order (act 1008). In one example, in the case where the model analytics are expressed as equations, constraints, and rules, the order of execution may be as follows 1) equations with dependencies or that are not fully solvable as an independent expression are rewritten as constraints 2) the constraints are solved, 3) the equations are solved, and 4) the rules are solved. The rules solving may cause the data to be updated.

Once the solvers are executed in the designated order, it is then determined whether or not solving should stop (decision block 1009). The solving process should stop if, for example, all of the output model variables are solved for, or if it is determined that even though not all of the output model variables are solved for, the specialized solvers can do nothing further to solve for any more of the output model variables. If the solving process should not end (No in decision block 1009), the process returns back to the analyzing of dependencies (act 1004). This time, however, the identity of the input and output model variables may have changed due to one or more output model variables being solved for. On the other hand, if the solving process should end (Yes in decision block 1009) the solve ends (act 1010). However, if a model cannot be fully solved because there are too many output model variables, the model nevertheless may succeed in generating a partial solution where the output model variables have been assigned symbolic values reflective of how far the solve was able to proceed. For example, if a model has an equation A=B+C, and B is known to be “2” and is an input model variable but C is an output model variable and A is also an output model variable and needs to be solved for, the model solver cannot product a numerical value for A since while B is known C is unknown; so instead of a full solve, the solver returns “2+C” as the value for A. It is thus clear to the author what additional variable needs to become known, either by supplying it a value or by adding further rules/equations/constraints or simulations that can successfully produce the needed value from other input data.

This method 1000 may repeat each time the solver framework detects that there has been a change in the value of any of the known model parameters, and/or each time the solver framework determines that the identity of the known and unknown model parameters has changed. Solving can proceed in at least two ways. First, if a model can be fully solved symbolically (that is, if all equations, rules, and constraints can be algorithmically rewritten so that a computable expression exists for each unknown) then that is done, and then the model is computed. In other words, data values are generated for each unknown, and/or data values that are permitted to be adjusted are adjusted. As a second possible way, if a model cannot be fully solved symbolically, it is partially solved symbolically, and then it is determined if one or more numerical methods can be used to effect the needed solution. Further, an optimization step occurs such that even in the first case, it is determined whether use of numerical methods may be the faster way to compute the needed values versus performing the symbolic solve method. Although the symbolic method can be faster, there are cases where a symbolic solve may perform so many term rewrites and/or so many rewriting rules searches that it would be faster to abandon this and solve using numeric methods.

Composite View Composition

The pipeline environment 200 also includes a model importation mechanism 241 that is perhaps included as part of the authoring mechanism 240. The model importation mechanism 241 provides a user interface or other assistance to the author to allow the author to import at least a portion of a pre-existing analytics-driven model into the current analytics-driven model that the user is constructing. Accordingly, the author need not always begin from scratch when authoring a new analytics model. The importation may be of an entire analytics-driven model, or perhaps a portion of the model. For instance, the importation may cause one or more or all of the following six potential effects.

As a first potential effect of the importation, additional model input data may be added to the pipeline. For instance, referring to FIG. 2, additional data might be added to the input data 211, the analytics data 221 and/or the view data 231. The additional model input data might also include additional connectors being added to the data access component 310 of FIG. 3, or perhaps different canonicalization components 330.

As a second potential effect of the importation, there may be additional or modified bindings between the model input data and the model parameters. For instance, referring to FIG. 4, the data-model binder 410 may cause additional bindings to occur between the canonicalized data 401 and the model parameters 411. This may cause an increase in the number of known model parameters.

As a third potential effect of the importation, there may be additional model parameters to generate a supplemental set of model parameters. For instance, referring to FIG. 4, the model parameters 411 may be augmented due to the importation of the analytical behaviors of the imported model.

As a fourth potential effect of the importation, there may be additional analytical relationships (such as equations, rules and constraints) added to the model. The additional input data resulting from the first potential effect, the additional bindings resulting for the second potential effect, the additional model parameters resulting from the third potential effect, and the additional analytical relationships resulting from the fourth effect. Any one of more of these additional items may be viewed as additional data that affects the view composition. Furthermore, any one or more of these effects could change the behavior of the solver 440 of FIG. 4.

As a fifth potential effect of the importation, there may be additional or different bindings between the model parameters and the input parameters of the view. For instance, referring to FIG. 5, the model-view binding component 510 binds a potentially augmented set of model parameters 411 to a potentially augmented set of view components in the view component repository 520.

As a sixth potential effect of the importation, there may be additional parameterized view components added to the view component repository 520 of FIG. 5, resulting in perhaps new view items being added to the view composition.

Accordingly, by importing all or a portion of another model, the data associated with that model is imported. Since the view composition is data-driven, this means that the imported portions of the model are incorporated immediately into the current view composition.

When the portion of the pre-existing analytics-driven analytics model is imported, a change in data supplied to the pipeline 201 occurs, thereby causing the pipeline 201 to immediately, or in response to some other event, cause a regeneration of the view composition. Thus, upon what is essentially a copy and paste operation from an existing model, that resulting composite model might be immediately viewable on the display due to a resolve operation.

As an example of how useful this feature might be, consider the Feng Shui room view composition of FIG. 6. The author of this application may be a Feng Shui expert, and might want to just start from a standard room layout view composition model. Accordingly, by importing a pre-existing room layout model, the Feng Shui expert is now relatively quickly, if not instantly, able to see the room layout 601 show up on the display shown in FIG. 6. Not only that, but now the furniture and room item catalog that normally might come with the standard room layout view composition model, has now become available to the Feng Shui application of FIG. 6.

Now, the Feng Shui expert might want to import a basic pie chart element as a foundation for building the Feng Shui chart element 602. Now, however, the Feng Shui expert might specify specific fixed input parameters for the chart element including perhaps that there are 8 wedges total, and perhaps a background image and a title for each wedge. Now the Feng Shui expert need only specify the analytical relationships specifying how the model parameters are interrelated. Specifically, the color, position, and type of furniture or other room item might have an effect on a particular Feng Shui score. The expert can simply write down those relationships, to thereby analytically interconnect the room layout 601 and the Feng Shui score. This type of collaborative ability to build on the work of others may generate a tremendous wave of creativity in creating applications that solve problems and permit visual analysis. This especially contrasts with systems that might allow a user to visually program a one-way data flow using a fixed dependency graph. Those systems can do one-way solves, the way originally programmed from input to output. The principles described herein allow solves in multiple ways, depending on what is known and what is unknown at any time given the interactive session with the user.

Visual Interaction

The view composition process has been described until this point as being a single view composition being rendered at a time. For instance, FIG. 6 illustrates a single view composition generated from a set of input data However, the principles described herein can be extended to an example in which there is an integrated view composition that includes multiple constituent view compositions. This might be helpful in a number of different circumstances.

For example, given a single set of input data, when the solver mechanism is solving for output model variables, there might be multiple possible solutions. The constituent view compositions might each represent one of multiple possible solutions, where another constituent view composition might represent another possible solution.

In another example, a user simply might want to retain a previous view composition that was generated using a particular set of input data, and then modify the input data to try a new scenario to thereby generate a new view composition. The user might then want to retain also that second view composition, and try a third possible scenario by altering the input data once again. The user could then view the three scenarios at the same time, perhaps through a side-by-side comparison, to obtain information that might otherwise be difficult to obtain by just looking at one view composition at a time.

FIG. 11 illustrates an integrated view composition 1100 that extends from the Feng Shui example of FIG. 6. In the integrated view composition, the first view composition 600 of FIG. 6 is represented once again using elements 601 and 602, exactly as shown in FIG. 6. However, here, there is a second view composition that is emphasized represented. The second view composition is similar to the first view composition in the there are two elements, a room display and a Feng Shui score meter. However, the input data for the second view composition was different than the input data for the first view composition. For instance, in this case, the position data for several of the items of furniture would be different thereby causing their position in the room layout 1101 of the second view composition to be different than that of the room layout 601 of the first view composition. However, the different position of the various furniture items correlates to different Feng Shui scores in the Feng Shui meter 1102 of the second view composition as compared to the Feng Shui meter 602 of the first view composition.

The integrated view composition may also include a comparison element that visually represents a comparison of a value of at least one parameter across some of all of the previously created and presently displayed view compositions. For instance, in FIG. 11, there might be a bar graph showing perhaps the cost and delivery time for each of the displayed view compositions. Such a comparison element might be an additional view component in the view component repository 520. Perhaps that comparison view element might only be rendered if there are multiple view compositions being displayed. In that case, the comparison view composition input parameters may be mapped to the model parameters for different solving iterations of the model. For instance, the comparison view composition input parameters might be mapped to the cost parameter that was generated for both of the generations of the first and second view compositions of FIG. 11, and mapped to the delivery parameter that was generated for both of the generations of the first and second view compositions.

Referring to FIG. 11, there is also a selection mechanism 1110 that allows the user to visually emphasize a selected subset of the total available previously constructed view compositions. The selection mechanism 1110 is illustrated as including three possible view constructions 1111, 1112 and 1113, that are illustrated in thumbnail form, or are illustrated in some other deemphasized manner. Each thumbnail view composition 1111 through 1113 includes a corresponding checkbox 1121 through 1123. The user might check the checkbox corresponding to any view composition that is to be visually emphasized. In this case, the checkboxes 1121 and 1123 are checked, thereby causing larger forms of the corresponding view constructions to be displayed.

The integrated view composition, or even any single view composition for that matter, may have a mechanism for a user to interact with the view composition to designate what model parameters should be treated as an unknown thereby triggering another solve by the analytical solver mechanism. For instance, in the room display 1101 of FIG. 11, one might right click on a particular item of furniture, right click on a particular parameter (e.g., position), and a drop down menu might appear allowing the user to designate that the parameter should be treated as unknown. The user might then right click on the harmony percentage (e.g., 95% in the Feng Shui score meter 1102), whereupon a slider might appear (or a text box of other user input mechanism) that allows the user to designate a different harmony percentage. Since this would result in the identity of the known and unknown parameters being changed, a re-solve would result, and the item of furniture whose position was designated as an unknown might appear in a new location.

In one embodiment, the integrated view composition might also include a visual prompt for an adjustment that could be made that might trend a value of a model parameter in a particular direction. For example, in the Feng Shui example, if a particular harmony score is designated as a known input parameter, various positions of the furniture might be suggested for that item of furniture whose position was designated as an unknown. For instance, perhaps several arrows might emanate from the furniture suggesting a direction to move the furniture in order to obtain a higher harmony percentage, a different direction to move to maximize the water score, a different direction to move to maximum the water score, and so forth. The view component might also show shadows where the chair could be moved to increase a particular score. Thus, a user might use those visual prompts in order to improve the design around a particular parameter desired to be optimized. In another example, perhaps the user wants to reduce costs. The user might then designate the cost as an unknown to be minimized resulting in a different set of suggested furniture selections.

ADDITIONAL EXAMPLE APPLICATIONS

The architecture of FIGS. 1 and 2 may allow countless data-driven analytics model to be constructed, regardless of the domain. There is nothing at all that need be similar about these domains. Wherever there is a problem to be solved where it might be helpful to apply analytics to visuals, the principles described herein may be beneficial. Up until now, only a few example applications have been described including a Feng Shui room layout application. To demonstrate the wide-ranging applicability of the principles described herein, several additional wide-ranging example applications will now be described.

Additional Example #1 Retailer Shelf Arrangements

Product salespersons often use 3-D visualizations to sell retailers on shelf arrangements, end displays and new promotions. With the pipeline 201, the salesperson will be able to do what-ifs on the spot. Given some product placements and given a minimum daily sales/linear foot threshold, the salesperson may calculate and visualize the minimum required stock at hand. Conversely, given some stock at hand and given a bi-weekly replenishment cycle, the salesperson might calculate product placements that will give the desired sales/linear foot. The retailer will be able to visualize the impact, compare scenarios, and compare profits. FIG. 12 illustrates an example retailer shelf arrangement visualization. The input data might include visual images of the product, a number of the product, a linear square footage allocated for each product, and shelf number for each product, and so forth.

Additional Example #2 Urban Planning

Urban planning mash ups are becoming prominent. Using the principles described herein, analytics can be integrated into such solutions. A city planner will open a traffic model created by experts, and drag a bridge in from a gallery of road improvements. The bridge will bring with it analytical behavior like length constraints and high-wind operating limits. Via appropriate visualizations, the planner will see and compare the effect on traffic of different bridge types and placements. The principles described herein may be applied to any map scenarios where the map might be for a wide variety of purposes. The map might be for understanding the features of a terrain and finding directions to some location. The map might also be a visual backdrop for comparing regionalized data. More recently, maps are being used to create virtual worlds in which buildings, interiors and arbitrary 2-D or 3-D objects can be overlaid or positioned in the map. FIG. 13 illustrates an example visualized urban plan.

Additional Example #3 Visual Education

In domains like science, medicine, and demographics where complex data needs to be understood not just by domain practitioners but also the public, authors can use the principles described herein to create data visualizations that intrigue and engage the mass audience. They will use domain-specific metaphors, and impart the authors' sense of style. FIG. 14 is an illustration about children's education. FIG. 15 is a conventional illustration about population density. Conventionally, such visualizations are just static illustrations. With the principles described herein, these can become live, interactive experiences. For instance, by inputting a geographically distributed growth pattern as input data, a user might see the population peaks change. Some visualizations, where the authored model supports this, will let users do what-ifs. That is, the author may change some values and see the effect on that change on other values.

Accordingly, the principles described herein provide a major paradigm shift in the world of visualized problem solving and analysis. The paradigm shift applies across all domains as the principles described herein may apply to any domain.

Domain-Specific Taxonomy of Data

Referring back to FIG. 2, the pipeline 201 is data-driven. For instance, input data 211 is provided to data portion 210, analytics data 221 is provided to analytics portion 220, and view data 231 is provided to view portion 230. Examples of each of these data have already been described. Suffice it to say that the volume of data that could be selected by the authoring component 240 may be quite large, especially given the ease of composition in which portions of models can be imported into a model to compose more and more complex models. To assist in navigating through the data, so that the proper data 211, 221 and 231 may be selected, a taxonomy component 260 provides a number of domain-specific taxonomies of the input data.

FIG. 16 illustrates a taxonomy environment 1600 in which the taxonomy component 260 may operate. Taxonomy involves the classification of items into categories and the relating of those categories. The environment 1600 thus includes a collection of items 1610 that are to be subjected to taxonomization. In FIG. 16, the collection of items 1610 is illustrated as including only a few items altogether including items 1611A through 1611P (referred to collectively as “member items 1611”). Although only a few member items 1611 are shown, there may be any number of items, perhaps even hundreds, thousands or even millions of items that should be categorized and taxonomized as represented by the ellipsis 1611Q. The member items 1611 include the pool of member items from which the authoring component 240 may select in order to provide the data 211, 221 and 231 to the pipeline 201.

A domain-sensitive taxonomization component 1620 accesses all or a portion of the member items 1611, and also is capable of generating a distinct taxonomy of the member items 1611. For instance, the taxonomization component 1620 generates domain specific taxonomies 1621. In this case, there are five domain-specific taxonomies 1621A through 1621E, amongst potentially others as represented by the ellipsis 1621F. There may also be fewer than five domain-specific taxonomies created and managed by the taxonomization component 1620.

As an example, the taxonomy 1621A might taxonomize the member items suitable for a Feng Shui domain, taxonomy 1621B may taxonomize the member items suitable for a motorcycle design domain, taxonomy 1621C may likewise be suitable for a city planning domain, taxonomy 1621D may be suitable for an inventory management domain, and taxonomy 1621E may be suitable for an abstract artwork domain. Of course, these are just five of the potentially countless number of domains that may be served by the pipeline 201. Each of the taxonomies may use all or a subset of the available member items to classify in the corresponding taxonomy.

FIG. 17 illustrates one specific and simple example 1700 of a taxonomy of the member items. For example, the taxonomy may be the domain-specific taxonomy 1621A of FIG. 16. Subsequent figures will set forth more complicated examples. The taxonomy 1700 includes category node 1710 that includes all of the member items 1611 except member items 1611A and 1611E. The category node 1710 may be an object that, for example, includes pointers to the constituent member items and thus in the logical sense, the member items may be considered “included within” the category node 1710. The category node 1710 also has associated therewith a properties correlation descriptor 1711 that describes the membership qualifications for the category node 1710 using the properties of the candidate member items. When determining whether or not a member item should be included in a category, the properties correlation descriptor may be used to evaluate the descriptor against the properties of the member item.

In a taxonomy, two categories can be related to each other in a number of different ways. One common relation is that one category is a subset of another. For example, if there is a “vehicle” category that contains all objects that represent vehicles, there might be a “car” category that contains a subset of the vehicles category. The property correlation descriptors of both categories may define the specific relationship. For instance, the property correlation descriptor for the vehicles category may indicate that objects having the following properties will be included in the category: 1) the object is movable, 2) the object may contain a human. The car category property correlation descriptor may include these two property requirements either expressly or implicitly, and may also include the following property requirements: 1) the object contains at least 3 wheels that maintain contact with the earth during motion of the object, 2) the object is automotive, 3) the height of the object does not exceed 6 feet. Based on the property correlation descriptors for each category, the taxonomization component may assign an object to one or more categories in any given domain-specific taxonomy, and may also understand the relationship between the categories.

In FIG. 17, a second category node 1720 is shown that includes another property correlation descriptor 1721. The category node 1720 logically includes all member items that satisfy the property correlation descriptor 1721. In this case, the member items logically included in the category node are a subset of the member items included in the first category node 1710 (e.g. including member items 1611F, 1611J, 1611N and 1611P). This could be because the property correlation descriptor 1721 of the second category node 1720 specifies the same property requirements as the correlation descriptor 1711 of the first category node 1710, except for one or more additional property requirements. The relation between the first category node 1710 and the second category node 1720 is logically represented by relation 1715.

In a vehicle-car example, the relationship between the categories is a subset relation. That is, one category (e.g., the car category) is a subset of the other (e.g., the vehicle category). However, there are a wide variety of other types of relationships as well, even perhaps new relationships that have never been recognized or used before. For example, there might be a majority inheritance relationship in which if a majority (or some specified percentage) of the objects in one category have a particular property value, the objects in another category have this property and inherit this property value. There might be a “similar color” relationship in which if one category of objects has a primary color within a certain wavelength range of visible light, then the other category contains objects having a primary color within a certain neighboring wavelength range of visible light. There might be a “virus mutation” relationship in which if one category contains objects that represent certain infectious diseases that are caused primarily by a particular virus, a related category might include objects that represent certain infectious diseases that are caused by a mutated form of the virus. The examples could go on and on for volumes. One of ordinary skill in the art will recognize after having reviewed this description, that the kinds of relationships between categories is not limited.

Further a single taxonomy can have many different types of relations. For clarity, various taxonomies will now be described in an abstract sense. Examples of abstractly represented taxonomies are shown in FIGS. 18A through 18C. Then specific examples will be described, understanding that the principles described herein enable countless applications of domain-specific taxonomies in a data-driven visualization.

While the example of FIG. 17 is a simple two category node taxonomy, the examples of FIGS. 18A through 18C are more complex. Each node in the taxonomies 1800A through 1800C of FIGS. 18A through 18C represents a category node that contains zero or more member items, and may have a property correlation descriptor associated with each that is essentially an admission policy for admitting member items into the category node. To avoid undue complexity, however, the member items and property correlation descriptor for each of the category nodes of the taxonomies 1800A through 1800C are not illustrated. The lines between the category nodes represent the relations between category nodes. They might be a subset relation or some other kind of relation without limit. The precise nature of the relations between category nodes is not critical. Nevertheless, to emphasize that there may be a variety of relation types between category nodes in the taxonomy, the relations are labeled with an A, B, C, D, or E.

FIGS. 18A through 18C are provided just as an example. The precise structure of the taxonomies of FIGS. 18A through 18C is not only not critical, but the principles described herein permit great flexibility in what kinds of taxonomies can be generated even based on the same set of input candidate member items. In these examples, the taxonomy 1800A includes category node 1801A through 1810A related to each other using relation types A, B and C. Taxonomy 1800B includes categories 1801B through 1808B related to each other using relation types B, C and D. Taxonomy 1800C includes categories 1801C through 1812C related to each other using relation types C, D and E. In this example, taxonomies 1800A and 1800B are hierarchical, whereas taxonomy 1800C is more of a non-hierarchical network.

As new candidate member items become available, those candidate member items may be evaluated against the property correlation descriptor of each of the category nodes in each the taxonomies. If the properties of the member item have values that permit the member item to satisfy the requirements of the property correlation descriptor (i.e., the admission policy), the member item is admitted into the category node. For instance, perhaps a pointer to the member item is added to the category node. Thus, if new member items have sufficient numbers of properties, new member items may be imported automatically into appropriate categories in all of the taxonomies.

FIG. 19 illustrates a member item 1900 that includes multiple properties 1901. There might be a single property, but there might also be potentially thousands of properties associated with the member item 1900. In FIG. 19, the member item 1900 is illustrated as including four properties 1901A, 1901B, 1901C and 1901D, amongst potentially others as represented by the ellipsis 1901F. There is no limit to what these properties may be. They might be anything that might be useful in categorizing the member item into a taxonomy.

In one embodiment, potential data for each of the data portion 210, the analytics portion 220, and the view portion 230 may be taxonomized. For example, consider the domain in which the author is composing a consumer application that allows an individual (such as a consumer or neighborhood resident) to interface with a map of a city.

In this consumer domain, there might be a taxonomy for view data 231 that can be selected. For instance, there might be a building category that includes all of the buildings. There might be different types of buildings: government buildings, hospitals, restaurants, houses, and so forth. There might also be a transit category that includes railroad, roadways, and canals sub-categories. The roadways category might contain categories or objects representing streets, highways, bike-paths, overpasses, and so forth. The streets category might include objects or categories of visual representations of one way streets, multi-line streets, turn lanes, center lanes, and so forth. There might be a parking category showing different types of visual representations of parking or other sub-categories of parking (e.g., multi-level parking, underground parking, street parking, parking lots, and so forth). Parking might also be sub-categorized by whether or not parking is free, or whether there is a cost.

There might also be a taxonomy of the input data in this consumer domain. For instance, a parking structure might have data associated with it such as, for example, 1) whether the parking is valet parking, 2) what the hourly change is for the parking, 3) the hours that the parking is open, 4) whether the parking is patrolled by security, and if so, how many security officers there are per unit area of parking, 5) the number of levels of the parking, 6) the square footage of the parking if there is but one level, and if multi-level parking, the square footage on each level, 7) the annualized historical number of car thefts that occur in the parking structure, 8) the volume usage of the parking, 9) whether parking is restricted to the satisfaction of one or more conditions (i.e., employment at a nearby business, patronage at a restaurant or mall, and so forth), or any other data that might be helpful. There might also be data associated with other visual items as well, and data that may never affect how the visual items is rendered, but might be used for a calculation at some point.

However, there might also be a taxonomy of the analytics data 221 that is specific to this consumer map domain. For instance, the analytics might present cost-based analytics in one category, time-based analytics in another category, distance-based analytics in yet another category, directory analytics in another category, and routing analytics in another category. Here, the analytics are taxonomized to assist the author in formulating an analytical model for the desired application. For instance, the routing analytics category might include a category for equations that calculate a route, a constraint that specifies what restrictions can be made on the routing (such as shortest route, most use of highways, avoid streets, and so forth), or rules (such as traffic directions on particular roads). Similar subcategories might also be included for the other categories as well.

Now consider another domain, also dealing with the layout of a city, but this time, the domain is city planning. Here, there are analytics that are interesting to city planners that are of little to no interest to a consumer. For instance, there might be analytics that calculate how thick the pavement should be given a certain traffic usage, what the overall installation and maintenance cost per linear foot of a certain road type placed in a certain area, what the safety factor of a bridge is given expected traffic patterns forecast for the next 20 years, what the traffic bottlenecks are in the current city plan, what the environmental impact would be if a particular building was constructed in a particular location, what the impact would be if certain restrictions were placed on the usage of that particular building and so forth. Here, the problems to be solved are different than those to be solved in the consumer domain. Accordingly, the taxonomy of the analytics may be laid out much differently for the city planning domain as compared to the consumer domain, even though both deal with a city topology.

On the other hand, a tractor design domain might be interested in a whole different set of analytics, and would use a different taxonomy. For instance, the visual items of a tractor design domain might be totally different than that of a city planning domain. No longer is there a concern for city visual elements. Now, the various visual elements that make up a tractor are taxonomized. As an example, the visual items might be taxonomized using a relation of what can be connected to what. For instance, there might be a category for “Things that can be connected to the seat”, “Things that can be connected to the carburetor, “Things that can be connected to the rear axle” and so forth. There might also be a different analytics taxonomy. For instance, there might be a constraint regarding tread depth on the tires considering that the tractor needs to navigate through wet soil. There might be analytics that calculate the overall weight of the tractor or its subcomponents, and so forth.

FIG. 20 illustrates a domain-specific taxonomy 2000 and represents one example of the domain-specific taxonomies 1621 of FIG. 16. In one embodiment, the domain-specific taxonomy includes a data taxonomy 2001 in which at least some of the available data items are taxonomized into corresponding related categories, a view component taxonomy 2003 in which at least some of the available view components are taxonomized into a corresponding related view components categories, and an analytics taxonomy 2002 in which at least some of the available analytics are taxonomized into correlating related analytics categories. Examples of such domain specific taxonomies in which data, analytics, and view components are taxonomized in a manner that is specific to domain have already been described.

FIG. 21 illustrates a method for navigating and using analytics. The analytics component 220 is accessed (act 2101) along with the corresponding domain specific analytics taxonomy (act 2103). If there are multiple domain-specific analytics taxonomies, the domain may first be identified (act 2102), before the domain-specific analytics taxonomy may be accessed (act 2103).

Then, the analytics taxonomy may be navigated (act 2104) by traversing the related categories. This navigation may be performed by a human being with the assistance of a computing system, or may be performed even by a computing system alone without the contemporaneous assistance of a human being. A computer or human may derive information from the correlation property descriptor for each category that defines that admission policy for analytics to be entered into that category. Information may also be derived by the relationships between categories. The navigation may be used to solve the analytics problem thereby solving for output model parameters, or perhaps for purposes of merging analytics from multiple models. Alternatively, the navigation may be used to compose the analytics model in the first place.

For instance, suppose an analytics taxonomy categorizes relations in terms of the identity of the type of problem to be solved. The composer could begin by reviewing all of those analytics in the problem type category of interest. That category might have related categories that define portions of the problem to be solved. The user could quickly navigate to those related categories and find analytics that are of relevance in the domain.

Having described the embodiments in some detail, as a side-note, the various operations and structures described herein may, but need, not be implemented by way of a computing system. Accordingly, to conclude this description, an example computing system will be described with respect to FIG. 22.

FIG. 22 illustrates a computing system 2200. Computing systems are now increasingly taking a wide variety of forms. Computing systems may, for example, be handheld devices, appliances, laptop computers, desktop computers, mainframes, distributed computing systems, or even devices that have not conventionally been considered a computing system. In this description and in the claims, the term “computing system” is defined broadly as including any device or system (or combination thereof) that includes at least one processor, and a memory capable of having thereon computer-executable instructions that may be executed by the processor. The memory may take any form and may depend on the nature and form of the computing system. A computing system may be distributed over a network environment and may include multiple constituent computing systems.

As illustrated in FIG. 22, in its most basic configuration, a computing system 2200 typically includes at least one processing unit 2202 and memory 2204. The memory 2204 may be physical system memory, which may be volatile, non-volatile, or some combination of the two. The term “memory” may also be used herein to refer to non-volatile mass storage such as physical storage media. If the computing system is distributed, the processing, memory and/or storage capability may be distributed as well. As used herein, the term “module” or “component” can refer to software objects or routines that execute on the computing system. The different components, modules, engines, and services described herein may be implemented as objects or processes that execute on the computing system (e.g., as separate threads).

In the description that follows, embodiments are described with reference to acts that are performed by one or more computing systems. If such acts are implemented in software, one or more processors of the associated computing system that performs the act direct the operation of the computing system in response to having executed computer-executable instructions. An example of such an operation involves the manipulation of data. The computer-executable instructions (and the manipulated data) may be stored in the memory 2204 of the computing system 2200.

Computing system 2200 may also contain communication channels 2208 that allow the computing system 2200 to communicate with other message processors over, for example, network 2210. Communication channels 2208 are examples of communications media. Communications media typically embody computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism and include any information-delivery media. By way of example, and not limitation, communications media include wired media, such as wired networks and direct-wired connections, and wireless media such as acoustic, radio, infrared, and other wireless media. The term computer-readable media as used herein includes both storage media and communications media.

Embodiments within the scope of the present invention also include computer-readable media for carrying or having computer-executable instructions or data structures stored thereon. Such computer-readable media can be any available media that can be accessed by a general purpose or special purpose computer. By way of example, and not limitation, such computer-readable media can comprise physical storage and/or memory media such as RAM, ROM, EEPROM, CD-ROM or other optical disk storage, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to carry or store desired program code means in the form of computer-executable instructions or data structures and which can be accessed by a general purpose or special purpose computer. When information is transferred or provided over a network or another communications connection (either hardwired, wireless, or a combination of hardwired or wireless) to a computer, the computer properly views the connection as a computer-readable medium. Thus, any such connection is properly termed a computer-readable medium. Combinations of the above should also be included within the scope of computer-readable media.

Computer-executable instructions comprise, for example, instructions and data which cause a general purpose computer, special purpose computer, or special purpose processing device to perform a certain function or group of functions. Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described herein. Rather, the specific features and acts described herein are disclosed as example forms of implementing the claims.

The present invention may be embodied in other specific forms without departing from its spirit or essential characteristics. The described embodiments are to be considered in all respects only as illustrative and not restrictive. The scope of the invention is, therefore, indicated by the appended claims rather than by the foregoing description. All changes which come within the meaning and range of equivalency of the claims are to be embraced within their scope. 

1. A method for composing a data-driven analytics model that includes at least an analytical modeling component that defines analytical relationships between the plurality of model parameters using a plurality of analytical relations, wherein the analytical modeling component uses the plurality of analytical relations to identify which of the plurality of model parameters are known and which are unknown, and solves for the identified unknown model parameter(s), the method comprising: an act of navigating through an analytics taxonomy in which analytical relations are taxonomized into corresponding related analytics categories; and an act of composing the plurality of analytical relations as a result of the act of navigating through the analytics taxonomy.
 2. The method in accordance with claim 1, wherein the analytics categories are related in a manner that suggests an order of assembly of the plurality of analytical relations.
 3. The method in accordance with claim 1, wherein at least some of the plurality of analytical relations are equations.
 4. The method in accordance with claim 1, wherein at least some of the plurality of analytical relations are constraints.
 5. The method in accordance with claim 1, wherein at least some of the plurality of analytical relations are rules.
 6. The method in accordance with claim 1, wherein at least some of the plurality of analytical relations are simulations.
 7. The method in accordance with claim 1, wherein the method is performed by a computing system without contemporaneous assistance from a human being.
 8. The method in accordance with claim 1, wherein the method is performed by a computing system at least in part at the contemporaneous direction of a human being.
 9. The method in accordance with claim 1, wherein the data-driven analytics model further comprises: a data source that is a source for a plurality of data items; and a data-model binding component configured to bind portions of the plurality of data items to corresponding model parameters of the plurality of model parameters.
 10. The method in accordance with claim 9, wherein the data-driven analytics model further comprises a data taxonomy in which at least some of the plurality of data items are taxonomized into a corresponding one of a plurality of related data categories, the method further comprising: an act of navigating through the data taxonomy; and an act of binding the plurality of data items to the plurality of model parameters using the data taxonomy navigated in the act of navigating.
 11. The method in accordance with claim 10, wherein the data-driven analytics model further comprises: a view components repository that is capable of containing a plurality of heterogenic view components, each corresponding to a visual item that may be displayed, and at least some of which being parameterized; a model-visual binding component configured to bind the plurality of model parameter values to parameter(s) of at least one of the plurality of parameterized view components contained within the view components repository; and a view composition module configured to formulate instructions for rendering a view that contains at least some of the visual items corresponding to the plurality of heterogenic view components.
 12. The method in accordance with claim 11, the data-driven analytics model further comprising a view component taxonomy in which at least some of the plurality of view components are taxonomized into a corresponding one of a plurality of related view component categories, the method further comprising: an act of navigating through the view component taxonomy; and an act of binding the plurality of model parameters to the plurality of view components using the navigated view component taxonomy.
 13. The method in accordance with claim 1, wherein the data-driven analytics model further comprises: a view components repository that is capable of containing a plurality of heterogenic view components, each corresponding to a visual item that may be displayed, and at least some of which being parameterized; a model-visual binding component configured to bind the plurality of model parameter values to parameter(s) of at least one of the plurality of parameterized view components contained within the view components repository; and a view composition module configured to formulate instructions for rendering a view that contains at least some of the visual items corresponding to the plurality of heterogenic view components.
 14. The method in accordance with claim 13, the data-driven analytics model further comprising a view component taxonomy in which at least some of the plurality of view components items are taxonomized into a corresponding one of a plurality of related view component categories, the method further comprising: an act of navigating through the view component taxonomy; and an act of binding the plurality of model parameters to the plurality of view components using the navigated view component taxonomy.
 15. A computer program product comprising one or more physical computer-readable media having thereon one or more computer-executable instruction(s) that, when executed by one or more processors of the computing system, cause the computing system to perform a method for managing multiple domain-specific taxonomies, the method comprising: an act of formulating a first domain-specific taxonomy of items; an act of associating the first domain-specific taxonomy of items with a first domain; an act of formulating a second domain-specific taxonomy of items; and an act of associating the second domain-specific taxonomy of items with a second domain that is different than the first domain.
 16. The computer program product in accordance with claim 15, the method further comprising: an act of determining which of the first or second domains is active; and an act of using the appropriate first or second associated domain-specific taxonomies depending on which of the first or second domains is active.
 17. The computer program product in accordance with claim 15, wherein the items included in the first domain-specific taxonomy and the items included in the second domain-specific taxonomy are at least partially overlapping.
 18. The computer program product in accordance with claim 15, wherein at least one category relationship of the first domain-specific taxonomy is not included in the second domain-specific taxonomy.
 19. The computer program product in accordance with claim 15, wherein the items included in the first domain-specific taxonomy and the items included in the second domain-specific taxonomy include at least one analytical relation.
 20. A computer program product comprising one or more physical computer-readable media having thereon computer-executable instructions that, when executed by one or more processors of a computing system, cause the computing system to formulate the following: an analytics portion that includes an analytical modeling component that defines analytical relationships between the plurality of model parameters using a plurality of analytical relations, wherein the analytical modeling component uses the plurality of analytical relations to identify which of the plurality of model parameters are known and which are unknown, and solves for the identified unknown model parameter(s); a data portion that includes a source that is a source for a plurality of data items; and a data-model binding component configured to bind portions of the plurality of data items to corresponding model parameters of the plurality of model parameters; a view portion that includes a view components repository that is capable of containing a plurality of heterogenic view components, each corresponding to a visual item that may be displayed, and at least some of which being parameterized; a model-visual binding component configured to bind the plurality of model parameter values to parameter(s) of at least one of a plurality of parameterized view components contained within the view components repository; and a view composition module configured to formulate instructions for rendering a view that contains at least some of the visual items corresponding to the plurality of heterogenic view components; and a taxonomy component configured to maintain analytics taxonomy in which analytical relations including the plurality of analytical relations are taxonomized into corresponding related analytics categories; a data taxonomy in which at least some of the plurality of data items are taxonomized into corresponding related data categories; and a view component taxonomy in which at least some of the plurality of view components items are taxonomized into corresponding related view components, wherein the taxonomy component further allows the user to navigate through the analytics taxonomy for purposes of composing the analytics portion of a data-driven analytics model, to navigate through the data taxonomy for purposes of binding the plurality of data items to the plurality of model parameters, and to navigate through the view component taxonomy for purposes of binding the plurality of model parameters to the plurality of view components. 